The Type 1 Diabetes Genetics Consortium (T1DGC) (www.t1dgc.org) was established to facilitate the completion of large-scale multi-disciplinary gene mapping projects targeting type 1 diabetes (T1D) that are generally beyond the scope of individual laboratories. The T1DGC has recently completed a genome-wide association scan (GWAS) of T1D, and combined the results in a meta-analysis with two previously published studies, identifying 41 distinct genomic locations with P <10-6. After excluding already established regions of association to T1D, the most significant SNPs in the remaining sites were tested for replication in an independent set of T1D cases, controls and families. Eighteen of these replicated with P <0.01 and provided genome-wide significance (P d 5 x 10-8) in a joint analysis of GWAS and replication data. Overall, the new T1DGC genome-wide association study of T1D provides evidence for more than 40 confirmed non- HLA T1D risk loci. Based on consideration of the decay of linkage disequilibrium around the most strongly associated SNPs in these T1D associated regions, the median number of genes in a region is 4 but ranges from 0 to 27 presenting a significant challenge for identifying the relevant genes and risk variants. High density SNP mapping and re-sequencing holds promise for further refining these intervals, but the low incidence of T1D in individuals of other than European ancestry may limit the power of genetic mapping approaches to identify risk variants. Even if risk variants are identified, fine mapping alone cannot provide insights into their possible mechanisms of action. In this application from the T1DGC, we propose a complementary approach to fine mapping, an integrative biology approach in which we will identify a limited set of potential causative variants within different regions based upon their effect on intermediate phenotypes. The approach includes proteomics to identify protein-protein interaction networks that might plausibly implicate a particular gene or pathway with disease relevance and transcript analysis to identify to identify allelic effects on mRNA levels either in cis (i.e., affecting a nearby gene in the region) or in trans (affecting another gene either already known to be involved in disease etiology, or in an associated region). Such "systems genetics" approaches to define regulation of gene expression have proven to be an effective tool for gene identification in animal models but have been somewhat less extensively applied in humans. In this application, we propose to apply these complementary approaches to identify SNPs within the genomic regions currently implicated in T1D pathogenesis that are associated with functional effects on the transcriptome or proteome. Our hypothesis is that characterization of the phenotypic effects of SNPs on gene expression or on protein function or interaction will provide a more efficient approach to the identification of risk variants in these regions than by mapping alone and will provide insights into possible mechanisms whereby these variants modify disease risk. PUBLIC HEALTH RELEVANCE: Type 1 diabetes (T1D) develops when the insulin-secreting cells in the pancreas are depleted by an autoimmune process of unknown origin. While insulin treatment for T1D is life-saving, development of effective preventive therapies could be enhanced by a better understanding of the underlying disease mechanism, particularly events occurring during the extended pre-clinical period. The proposed studies in this application will characterize newly discovered genetic risk loci for T1D which may serve as useful biomarkers for prediction of disease or as targets for therapy.